Random Subsampling google
Random sub­sampling, which is also known as Monte Carlo crossvalidation, as multiple holdout or as repeated evaluation set, is based on randomly splitting the data into subsets, whereby the size of the subsets is defined by the user. The random partitioning of the data can be repeated arbitrarily often. In contrast to a full crossvalidation procedure, random subsampling has been shown to be asymptotically consistent resulting in more pessimistic predictions of the test data compared with crossvalidation. The predictions of the test data give a realistic estimation of the predictions of external validation data . …

Goedel Machine google
Can machines design Can they come up with creative solutions to problems and build tools and artifacts across a wide range of domains Recent advances in the field of computational creativity and formal Artificial General Intelligence (AGI) provide frameworks for machines with the general ability to design. In this paper we propose to integrate a formal computational creativity framework into the G\’odel machine framework. We call this machine a design G\’odel machine. Such a machine could solve a variety of design problems by generating novel concepts. In addition, it could change the way these concepts are generated by modifying itself. The design G\’odel machine is able to improve its initial design program, once it has proven that a modification would increase its return on the utility function. Finally, we sketch out a specific version of the design G\’odel machine which specifically aims at the design of complex software and hardware systems. Future work could be the development of a more formal version of the Design G\’odel machine and a potential implementation. …

Layer-Wise Relevance Propagation (LRP) google
Despite the tremendous achievements of deep convolutional neural networks~(CNNs) in most of computer vision tasks, understanding how they actually work remains a significant challenge. In this paper, we propose a novel two-step visualization method that aims to shed light on how deep CNNs recognize images and the objects therein. We start out with a layer-wise relevance propagation (LRP) step which estimates a pixel-wise relevance map over the input image. Following, we construct a context-aware saliency map from the LRP-generated map which predicts regions close to the foci of attention. We show that our algorithm clearly and concisely identifies the key pixels that contribute to the underlying neural network’s comprehension of images. Experimental results using the ILSVRC2012 validation dataset in conjunction with two well-established deep CNNs demonstrate that combining the LRP with the visual salience estimation can give great insight into how a CNNs model perceives and understands a presented scene, in relation to what it has learned in the prior training phase. …

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